Research Article
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Year 2025, Volume: 9 Issue: 1, 181 - 195, 26.02.2025
https://doi.org/10.30518/jav.1599331

Abstract

References

  • Angelelli, E., Archetti, C., & Peirano, L. (2020). A matheuristic for the air transportation freight forwarder service problem. Computers & Operations Research, 123, 105002.
  • Archetti, C., & Peirano, L. (2020). Air intermodal freight transportation: The freight forwarder service problem. Omega, 94, 102040.
  • Bartle, J. R., Lutte, R. K., & Leuenberger, D. Z. (2021). Sustainability and air freight transportation: Lessons from the global pandemic. Sustainability, 13(7), 3738.
  • Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368.
  • Budak, A., Kaya, I., Karaşan, A., & Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing, 92, 106322.
  • Bunahri, R. R., Supardam, D., Prayitno, H., & Kuntadi, C. (2023). Determination of Air Cargo Performance: Analysis of Revenue Management, Terminal Operations, and Aircraft Loading (Air Cargo Management Literature Review). Dinasti International Journal of Management Science, 4(5), 833-844.
  • Burstein, G., & Zuckerman, I. (2023). Deconstructing risk factors for predicting risk assessment in supply chains using machine learning. Journal of Risk and Financial Management, 16(2), 97.
  • Can Saglam, Y., Yildiz Çankaya, S., & Sezen, B. (2021). Proactive risk mitigation strategies and supply chain risk management performance: an empirical analysis for manufacturing firms in Turkey. Journal of Manufacturing Technology Management, 32(6), 1224-1244.
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
  • Dauer, J. C., & Dittrich, J. S. (2022). Automated cargo delivery in low altitudes: concepts and research questions of an operational-risk-based approach. Automated Low-Altitude Air Delivery: Towards Autonomous Cargo Transportation with Drones, 3-23.
  • Davydenko, I. Y., Hopman, W. M. M., & Smokers, R. T. M. (2020). Carbon Footprinting of Combined Passenger Freight Operations in Aviation Networks. TNO: The Hague, The Netherlands.
  • De Oliveira, U. R., Dias, G. C., & Fernandes, V. A. (2024). Evaluation of a conceptual model of supply chain risk management to import/export process of an automotive industry: an action research approach. Operations Management Research, 17(1), 201-219.
  • Esmizadeh, Y., & Mellat Parast, M. (2021). Logistics and supply chain network designs: incorporating competitive priorities and disruption risk management perspectives. International Journal of Logistics Research and Applications, 24(2), 174-197.
  • Gao, F., Wang, W., Bi, C., Bi, W., & Zhang, A. (2023). Prioritization of used aircraft acquisition criteria: A fuzzy best–worst method (bwm)-based approach. Journal of Air Transport Management, 107, 102359.
  • Giuffrida, M., Jiang, H., & Mangiaracina, R. (2021). Investigating the relationships between uncertainty types and risk management strategies in cross-border e-commerce logistics. The International Journal of Logistics Management, 32(4), 1406-1433
  • Göçmen, E. (2021). Smart airport: evaluation of performance standards and technologies for a smart logistics zone. Transportation Research Record, 2675(7), 480-490.
  • Gritsenko, S., & Karpun, O. (2020). Creation of aviation transport and logistic clusters network. Intellectualization of logistics and supply chain management, (2), 7-15.
  • Han, P., Yang, X., Zhao, Y., Guan, X., & Wang, S. (2022). Quantitative ground risk assessment for urban logistical unmanned aerial vehicle (UAV) based on bayesian network. Sustainability, 14(9), 5733.
  • Hohenstein, N. O. (2022). Supply chain risk management in the COVID-19 pandemic: strategies and empirical lessons for improving global logistics service providers’ performance. The International Journal of Logistics Management, 33(4), 1336-1365.
  • Hong, S. J., Kim, W., & Hiatt, B. (2025). Examining airport agility at air cargo hub airports. Journal of Air Transport Management, 122, 102710.
  • Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag.
  • Kaya, T., & Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38(6), 6577-6585.
  • Kondratenko, Y., Kondratenko, G., Sidenko, I., & Taranov, M. (2020, July). Fuzzy and evolutionary algorithms for transport logistics under uncertainty. In International Conference on Intelligent and Fuzzy Systems (pp. 1456- 1463). Cham: Springer International Publishing.
  • Mahdavi, I., Mahdavi-Amiri, N., Heidarzade, A., & Nourifar, R. (2008). Designing a model of fuzzy TOPSIS in multiple criteria decision making. Applied mathematics and Computation, 206(2), 607-617.
  • Merkert, R. (2023). Air Cargo and Supply Chain Management. In The Palgrave Handbook of Supply Chain Management (pp. 1-18). Cham: Springer International Publishing.
  • Mesquita, A. C., & Sanches, C. A. (2024). Air cargo load and route planning in pickup and delivery operations. Expert Systems with Applications, 249, 123711.
  • Mızrak, F., & Akkartal, G. R. (2023). Determining and evaluating the strategies of air cargo freight forwarders to increase business volume with AHP method. Journal of Aviation, 7(2), 226-232.
  • Pınar, A. (2021). q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection. Journal of Turkish Operations Management, 5(1), 588–597.
  • Pınar, A., & Boran, F. E. (2022). 3PL service provider selection with q-rung orthopair fuzzy based CODAS method. In q-Rung Orthopair Fuzzy Sets: Theory and Applications (pp. 285–301). Singapore: Springer Nature.
  • Pournader, M., Kach, A., & Talluri, S. (2020). A review of the existing and emerging topics in the supply chain risk management literature. Decision sciences, 51(4), 867-919.
  • Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549.
  • Sahoo, R., Pasayat, A. K., Bhowmick, B., Fernandes, K., & Tiwari, M. K. (2022). A hybrid ensemble learning-based prediction model to minimise delay in air cargo transport using bagging and stacking. International Journal of Production Research, 60(2), 644-660.
  • Sales, M., & Scholte, S. (2023). Air cargo management: Air freight and the global supply chain. Routledge.
  • Sencer, A., & Karaismailoglu, A. (2022). A simulation and analytic hierarchy process based decision support system for air cargo warehouse capacity design. Simulation, 98(3), 235-255.
  • Sun, X., Chung, S. H., & Ma, H. L. (2020). Operational risk in airline crew scheduling: do features of flight delays matter?. Decision Sciences, 51(6), 1455-1489.
  • Tanrıverdi, G., Ecer, F., & Durak, M. Ş. (2022). Exploring factors affecting airport selection during the COVID-19 pandemic from air cargo carriers’ perspective through the triangular fuzzy Dombi-Bonferroni BWM methodology. Journal of Air Transport Management, 105, 102302.
  • Tseremoglou, I., Bombelli, A., & Santos, B. F. (2022). A combined forecasting and packing model for air cargo loading: A risk-averse framework. Transportation Research Part E: Logistics and Transportation Review, 158, 102579.
  • Yalçın, S., & Ayyıldız, E. (2024). Prioritizing freight carrier selection factors with the best worst method. Central European Journal of Operations Research, 1-16.
  • Yan, Y., Chow, A. H., Ho, C. P., Kuo, Y. H., Wu, Q., & Ying, C. (2022). Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities. Transportation Research Part E: Logistics and Transportation Review, 162, 102712.

Prioritizing Risk Mitigation Strategies in Air Cargo Freight Operations: A Fuzzy TOPSIS Approach

Year 2025, Volume: 9 Issue: 1, 181 - 195, 26.02.2025
https://doi.org/10.30518/jav.1599331

Abstract

This study explores the prioritization of risk mitigation strategies in air cargo operations using a Fuzzy TOPSIS methodology. Air cargo operations face multifaceted risks, including operational inefficiencies, cybersecurity threats, regulatory compliance challenges, and environmental concerns. To address these, a structured decision-making framework was developed, integrating expert evaluations with fuzzy logic to rank mitigation strategies across ten criteria, such as cost-effectiveness, operational efficiency, and scalability. Enhanced Data Security Measures emerged as the top-ranked strategy, reflecting the critical importance of cybersecurity in modern logistics. Other highly prioritized strategies, including Resilience Building for Disruptions and Safety Enhancement Protocols, underscore the need for operational stability and safety in a rapidly evolving industry. The study demonstrates the practical applicability of Fuzzy TOPSIS in handling uncertainty and subjectivity in risk management while providing actionable insights for practitioners. Recommendations are offered for the implementation of prioritized strategies and the integration of emerging technologies, such as real-time analytics and AI-driven decision-making models. The findings contribute to advancing the field of risk management in air cargo operations and highlight areas for future research, including dynamic risk assessment and the integration of complementary MCDM techniques.

Ethical Statement

Etik onayı alınmıştır.

References

  • Angelelli, E., Archetti, C., & Peirano, L. (2020). A matheuristic for the air transportation freight forwarder service problem. Computers & Operations Research, 123, 105002.
  • Archetti, C., & Peirano, L. (2020). Air intermodal freight transportation: The freight forwarder service problem. Omega, 94, 102040.
  • Bartle, J. R., Lutte, R. K., & Leuenberger, D. Z. (2021). Sustainability and air freight transportation: Lessons from the global pandemic. Sustainability, 13(7), 3738.
  • Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368.
  • Budak, A., Kaya, I., Karaşan, A., & Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing, 92, 106322.
  • Bunahri, R. R., Supardam, D., Prayitno, H., & Kuntadi, C. (2023). Determination of Air Cargo Performance: Analysis of Revenue Management, Terminal Operations, and Aircraft Loading (Air Cargo Management Literature Review). Dinasti International Journal of Management Science, 4(5), 833-844.
  • Burstein, G., & Zuckerman, I. (2023). Deconstructing risk factors for predicting risk assessment in supply chains using machine learning. Journal of Risk and Financial Management, 16(2), 97.
  • Can Saglam, Y., Yildiz Çankaya, S., & Sezen, B. (2021). Proactive risk mitigation strategies and supply chain risk management performance: an empirical analysis for manufacturing firms in Turkey. Journal of Manufacturing Technology Management, 32(6), 1224-1244.
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
  • Dauer, J. C., & Dittrich, J. S. (2022). Automated cargo delivery in low altitudes: concepts and research questions of an operational-risk-based approach. Automated Low-Altitude Air Delivery: Towards Autonomous Cargo Transportation with Drones, 3-23.
  • Davydenko, I. Y., Hopman, W. M. M., & Smokers, R. T. M. (2020). Carbon Footprinting of Combined Passenger Freight Operations in Aviation Networks. TNO: The Hague, The Netherlands.
  • De Oliveira, U. R., Dias, G. C., & Fernandes, V. A. (2024). Evaluation of a conceptual model of supply chain risk management to import/export process of an automotive industry: an action research approach. Operations Management Research, 17(1), 201-219.
  • Esmizadeh, Y., & Mellat Parast, M. (2021). Logistics and supply chain network designs: incorporating competitive priorities and disruption risk management perspectives. International Journal of Logistics Research and Applications, 24(2), 174-197.
  • Gao, F., Wang, W., Bi, C., Bi, W., & Zhang, A. (2023). Prioritization of used aircraft acquisition criteria: A fuzzy best–worst method (bwm)-based approach. Journal of Air Transport Management, 107, 102359.
  • Giuffrida, M., Jiang, H., & Mangiaracina, R. (2021). Investigating the relationships between uncertainty types and risk management strategies in cross-border e-commerce logistics. The International Journal of Logistics Management, 32(4), 1406-1433
  • Göçmen, E. (2021). Smart airport: evaluation of performance standards and technologies for a smart logistics zone. Transportation Research Record, 2675(7), 480-490.
  • Gritsenko, S., & Karpun, O. (2020). Creation of aviation transport and logistic clusters network. Intellectualization of logistics and supply chain management, (2), 7-15.
  • Han, P., Yang, X., Zhao, Y., Guan, X., & Wang, S. (2022). Quantitative ground risk assessment for urban logistical unmanned aerial vehicle (UAV) based on bayesian network. Sustainability, 14(9), 5733.
  • Hohenstein, N. O. (2022). Supply chain risk management in the COVID-19 pandemic: strategies and empirical lessons for improving global logistics service providers’ performance. The International Journal of Logistics Management, 33(4), 1336-1365.
  • Hong, S. J., Kim, W., & Hiatt, B. (2025). Examining airport agility at air cargo hub airports. Journal of Air Transport Management, 122, 102710.
  • Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag.
  • Kaya, T., & Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38(6), 6577-6585.
  • Kondratenko, Y., Kondratenko, G., Sidenko, I., & Taranov, M. (2020, July). Fuzzy and evolutionary algorithms for transport logistics under uncertainty. In International Conference on Intelligent and Fuzzy Systems (pp. 1456- 1463). Cham: Springer International Publishing.
  • Mahdavi, I., Mahdavi-Amiri, N., Heidarzade, A., & Nourifar, R. (2008). Designing a model of fuzzy TOPSIS in multiple criteria decision making. Applied mathematics and Computation, 206(2), 607-617.
  • Merkert, R. (2023). Air Cargo and Supply Chain Management. In The Palgrave Handbook of Supply Chain Management (pp. 1-18). Cham: Springer International Publishing.
  • Mesquita, A. C., & Sanches, C. A. (2024). Air cargo load and route planning in pickup and delivery operations. Expert Systems with Applications, 249, 123711.
  • Mızrak, F., & Akkartal, G. R. (2023). Determining and evaluating the strategies of air cargo freight forwarders to increase business volume with AHP method. Journal of Aviation, 7(2), 226-232.
  • Pınar, A. (2021). q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection. Journal of Turkish Operations Management, 5(1), 588–597.
  • Pınar, A., & Boran, F. E. (2022). 3PL service provider selection with q-rung orthopair fuzzy based CODAS method. In q-Rung Orthopair Fuzzy Sets: Theory and Applications (pp. 285–301). Singapore: Springer Nature.
  • Pournader, M., Kach, A., & Talluri, S. (2020). A review of the existing and emerging topics in the supply chain risk management literature. Decision sciences, 51(4), 867-919.
  • Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549.
  • Sahoo, R., Pasayat, A. K., Bhowmick, B., Fernandes, K., & Tiwari, M. K. (2022). A hybrid ensemble learning-based prediction model to minimise delay in air cargo transport using bagging and stacking. International Journal of Production Research, 60(2), 644-660.
  • Sales, M., & Scholte, S. (2023). Air cargo management: Air freight and the global supply chain. Routledge.
  • Sencer, A., & Karaismailoglu, A. (2022). A simulation and analytic hierarchy process based decision support system for air cargo warehouse capacity design. Simulation, 98(3), 235-255.
  • Sun, X., Chung, S. H., & Ma, H. L. (2020). Operational risk in airline crew scheduling: do features of flight delays matter?. Decision Sciences, 51(6), 1455-1489.
  • Tanrıverdi, G., Ecer, F., & Durak, M. Ş. (2022). Exploring factors affecting airport selection during the COVID-19 pandemic from air cargo carriers’ perspective through the triangular fuzzy Dombi-Bonferroni BWM methodology. Journal of Air Transport Management, 105, 102302.
  • Tseremoglou, I., Bombelli, A., & Santos, B. F. (2022). A combined forecasting and packing model for air cargo loading: A risk-averse framework. Transportation Research Part E: Logistics and Transportation Review, 158, 102579.
  • Yalçın, S., & Ayyıldız, E. (2024). Prioritizing freight carrier selection factors with the best worst method. Central European Journal of Operations Research, 1-16.
  • Yan, Y., Chow, A. H., Ho, C. P., Kuo, Y. H., Wu, Q., & Ying, C. (2022). Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities. Transportation Research Part E: Logistics and Transportation Review, 162, 102712.
There are 39 citations in total.

Details

Primary Language English
Subjects Air Transportation and Freight Services
Journal Section Research Articles
Authors

Umit Kanmaz 0000-0003-2186-2737

Cengiz Kerem Kütahya This is me 0000-0002-7222-0921

Early Pub Date February 25, 2025
Publication Date February 26, 2025
Submission Date December 12, 2024
Acceptance Date February 1, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Kanmaz, U., & Kütahya, C. K. (2025). Prioritizing Risk Mitigation Strategies in Air Cargo Freight Operations: A Fuzzy TOPSIS Approach. Journal of Aviation, 9(1), 181-195. https://doi.org/10.30518/jav.1599331

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